Влияние внедрения искусственного интеллекта на финансовые показатели российских компаний

Год защиты: 2025.

Оценка: отлично.

Объем работы: 79 стр.

Оригинальность работы на момент публикации: 60+% по антиплагиат.вуз.

Цель данного исследования состоит в том, чтобы определить степень влияния внедрения искусственного интеллекта на финансовые результаты российских коммерческих компаний.

Для достижения цели исследования были поставлены следующие задачи:

1. Провести обзор теоретических исследований на тему влияния ИИ на финансовые результаты.

2. Собрать и обработать данные, а также рассчитать показатели по российским компаниям, внедрившим ИИ.

3. Разработать методологию регрессионных моделей с учетом особенностей зависимых переменных.

4. Определить спецификацию каждой модели на основании ряда эконометрических тестов.

5. Осуществить тестирование панельных регрессий для выявления влияния ИИ на финансовые показатели организаций и сравнить результаты с существующими зарубежными исследованиями.

1. Введение 3

2. Теоретические аспекты искусственного интеллекта и анализ его воздействия 6

2.1. Классификация искусственного интеллекта 7

2.2. Исследование положительных и отрицательных эффектов ИИ 8

2.3. Принципы успешного внедрения ИИ в бизнес-процессы 13

2.4. Формирование гипотез исследования 16

3. Методологические основы исследования и изучение ключевых параметров моделей 19

3.1. Структура данных и набор переменных 19

3.2. Разработка методологии 26

3.3. Предварительная обработка и описание данных 32

4. Эмпирическая оценка моделей 41

4.1. Алгоритм выбора спецификаций моделей 41

4.2. Анализ влияния ИИ на финансовые результаты компаний 44

4.2.1. Анализ влияния ИИ на рентабельность собственного капитала 44

4.2.2. Анализ влияния ИИ на прирост выручки 48

4.2.3. Анализ влияния ИИ на рентабельность активов 52

4.3. Проверка эндогенности 56

5. Заключение 58

6. Список литературы 61

7. Приложения 67

1. Попов В. В., (2024). Влияние применения искусственного интеллекта на различные отрасли экономики России: The Impact of Artificial Intelligence on Various Sectors of the Russian Economy. Applied Statistics and Artificial Intelligence, 2(2). https://doi.org/10.62302/ASAI.2024.2.2.006

2. Ратникова Т. А. Анализ панельных данных в пакете «STATA» Методические указания к компьютерному практикуму по курсу “Эконометрический анализ панельных данных”, 2004, НИУ ВШЭ (Кафедра математической экономики и эконометрики). URL: https://pokrovka11.wordpress.com/wp- content/uploads/2011/09/d0bcd0b0d182d0b5d180d0b8d0b0d0bbd18b-d0ba- d0bad0bed0bcd0bf-d0bfd180d0b0d0bad182d0b8d0bad183d0bcd183- rukovodstvo.pdf

3. Acemoglu, D. (2025). The simple macroeconomics of AI. Economic Policy, 40(121), 13–58. https://doi.org/10.1093/epolic/eiae042

4. AI Governance Alliance (2025). AI in Action: Beyond Experimentation to Transform Industry. World Economic Forum. URL: https://reports.weforum.org/docs/WEF_AI_in_Action_Beyond_Experimentation_to_Transform_Industry_2025.pdf

5. AI Governance Alliance (2025). Artificial Intelligence in Financial Services. World Economic Forum. URL: https://reports.weforum.org/docs/WEF_Artificial_Intelligence_in_Financial_Service s_2025.pdf

6. Alexopoulos, G. S. (2024). Artificial Intelligence in Geriatric Psychiatry Through the Lens of Contemporary Philosophy. The American Journal of Geriatric Psychiatry, 32(3), 293–299. https://doi.org/10.1016/j.jagp.2023.09.006

7. Babina, T., Fedyk, A., He, A., & Hodson, J. (2024). Artificial intelligence, firm growth, and product innovation. Journal of Financial Economics, 151, 103745. https://doi.org/10.1016/j.jfineco.2023.103745

8. Bahoo, S., Cucculelli, M., Goga, X., & Mondolo, J. (2024). Artificial intelligence in Finance: A comprehensive review through bibliometric and content analysis. SN Business & Economics, 4(2), 23. https://doi.org/10.1007/s43546-023-00618-x

9. Baltagi, B. H. (2021). Econometric Analysis of Panel Data. Springer International Publishing. https://doi.org/10.1007/978-3-030-53953-5

10. Barros, L., Castro, F. H., Da Silveira, A., & Bergmann, D. (2020). Endogeneity in panel data regressions: Methodological guidance for corporate finance researchers. Review of Business Management, 22(Special Issue), 437–461. https://doi.org/10.7819/rbgn.v22i0.4059

11. Baum, C. F. (2001). Residual Diagnostics for Cross-section Time Series Regression Models. The Stata Journal: Promoting Communications on Statistics and Stata, 1(1), 101–104. https://doi.org/10.1177/1536867X0100100108

12. Buntak, K., Kovacic, M., & Mutavdzija, M. (2021). APPLICATION OF ARTIFICIAL INTELLIGENCE IN THE BUSINESS. International Journal for Quality Research, 15(2), 403–416. https://doi.org/10.24874/IJQR15.02-03

13. Chen, D., Esperança, J. P., & Wang, S. (2022). The Impact of Artificial Intelligence on Firm Performance: An Application of the Resource-Based View to e-Commerce Firms. Frontiers in Psychology, 13, 884830. https://doi.org/10.3389/fpsyg.2022.884830

14. Chernysh, O., Smishko, O., Koverninska, Y., Prokopenko, M., & Pistunov, I. (2024). The Role of Artificial Intelligence in Financial Analysis and Forecasting: Using Data and Algorithms. Data and Metadata, 3. https://doi.org/10.56294/dm2024.224

15. Crompton, L. (2021). The decision-point-dilemma: Yet another problem of responsibility in human-AI interaction. Journal of Responsible Technology, 7–8, 100013. https://doi.org/10.1016/j.jrt.2021.100013

16. Damioli, G., Van Roy, V., & Vertesy, D. (2021). The impact of artificial intelligence on labor productivity. Eurasian Business Review, 11(1), 1–25. https://doi.org/10.1007/s40821-020-00172-8

17. Drukker, D. M. (2003). Testing for Serial Correlation in Linear Panel-data Models. The Stata Journal: Promoting Communications on Statistics and Stata, 3(2), 168–177. https://doi.org/10.1177/1536867X0300300206

18. Haefner, N., Parida, V., Gassmann, O., & Wincent, J. (2023). Implementing and scaling artificial intelligence: A review, framework, and research agenda. Technological Forecasting and Social Change, 197, 122878. https://doi.org/10.1016/j.techfore.2023.122878

19. Han, Y., Chen, J., Dou, M., Wang, J., & Feng, K. (2023). The Impact of Artificial Intelligence on the Financial Services Industry. Academic Journal of Management and Social Sciences, 2(3), 83–85. https://doi.org/10.54097/ajmss.v2i3.8741

20. Hashem, F., & Alqatamin, R. (2021). Role of Artificial Intelligence in Enhancing Efficiency of Accounting Information System and Non-Financial Performance of the Manufacturing Companies. International Business Research, 14(12), 65. https://doi.org/10.5539/ibr.v14n12p65

21. Huang, C., & Lin, J. (2025). Firm Performance on Artificial Intelligence Implementation. Managerial and Decision Economics, mde.4486. https://doi.org/10.1002/mde.4486

22. Huang, X. (2024). Analysis of the Impact of Artificial Intelligence on Enterprise Financial Accounting Work. Transactions on Economics, Business and Management Research, 7, 44–48. https://doi.org/10.62051/jhedh119

23. Ilaboya, Ofuan. J., & Ohiokha, Izien. F. (2016). Firm Age, Size and Profitability Dynamics: A Test of Learning by Doing and Structural Inertia Hypotheses. Business and Management Research, 5(1), p29. https://doi.org/10.5430/bmr.v5n1p29

24. Jankovic, S. D., & Curovic, D. M. (2023). Strategic Integration of Artificial Intelligence for Sustainable Businesses: Implications for Data Management and Human User Engagement in the Digital Era. Sustainability, 15(21), 15208. https://doi.org/10.3390/su152115208

25. Kim, T., Park, Y., & Kim, W. (2022). The Impact of Artificial Intelligence on Firm Performance. 2022 Portland International Conference on Management of Engineering and Technology (PICMET), 1–10. https://doi.org/10.23919/PICMET53225.2022.9882634

26. Kotios, D., Makridis, G., Fatouros, G., & Kyriazis, D. (2022). Deep learning enhancing banking services: A hybrid transaction classification and cash flow prediction approach. Journal of Big Data, 9(1), 100. https://doi.org/10.1186/s40537- 022-00651-x

27. Li, N., & Huang, F. (2024). Evaluating the Financial Returns on Investment in Sustainable Enterprise Digitalization Initiatives Using AI Information System. Journal of Internet Services and Information Security, 14(4), 21–44. https://doi.org/10.58346/JISIS.2024.I4.002

28. Maier, D. (2024). Is it worth the hype? Influence of Artificial Intelligence efforts on key financial company metrics. Copernican Journal of Finance & Accounting,

12(2). https://doi.org/10.12775/CJFA.2023.010

29. Makarius, E. E., Mukherjee, D., Fox, J. D., & Fox, A. K. (2020). Rising with the machines: A sociotechnical framework for bringing artificial intelligence into the organization. Journal of Business Research, 120, 262–273. https://doi.org/10.1016/j.jbusres.2020.07.045

 30. Mikalef, P., & Gupta, M. (2021). Artificial intelligence capability: Conceptualization, measurement calibration, and empirical study on its impact on organizational creativity and firm performance. Information & Management, 58(3), 103434. https://doi.org/10.1016/j.im.2021.103434

31. Mishra, S., Ewing, M. T., & Cooper, H. B. (2022). Artificial intelligence focus and firm performance. Journal of the Academy of Marketing Science, 50(6), 1176–1197. https://doi.org/10.1007/s11747-022-00876-5

32. Moro-Visconti, R., Cruz Rambaud, S., & López Pascual, J. (2023). Artificial intelligence-driven scalability and its impact on the sustainability and valuation of traditional firms. Humanities and Social Sciences Communications, 10(1), 795. https://doi.org/10.1057/s41599-023-02214-8

33. Mukosolu Okobo, M., Onuoha Ugwoke, R., & Etim Akpan, E. (2022). Investment in tangible non-current assets and financial performance of food manufacturing firms in Nigeria. Investment Management and Financial Innovations, 19(3), 360–372. https://doi.org/10.21511/imfi.19(3).2022.30

34. Perifanis, N.-A., & Kitsios, F. (2023). Investigating the Influence of Artificial Intelligence on Business Value in the Digital Era of Strategy: A Literature Review. Information, 14(2), 85. https://doi.org/10.3390/info14020085

35. Pillai N., Vijayamohanan, 2016. "Panel Data Analysis with Stata Part 1 Fixed Effects and Random Effects Models," MPRA Paper 76869, University Library of Munich, Germany.

36. Quispe, R., Rios, F., Quispe, F., Tafur, D., Vidal, R., & Mercedes, M. (2024). Impact of Artificial Intelligence (AI) on business financial management. SCIÉNDO, 27(2), 303–313. https://doi.org/10.17268/sciendo.2024.044

37. Rahman, A. (2021). Corporate Life Cycle and Firms’ Performance: An Empirical Study on DSE Listed Companies (IT Sector). SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3786851

38. Ramsey, J. B. (1969). Tests for Specification Errors in Classical Linear Least- Squares Regression Analysis. Journal of the Royal Statistical Society Series B: Statistical Methodology, 31(2), 350–371. https://doi.org/10.1111/j.2517- 6161.1969.tb00796.x

39. Rao, P., Srivastava, N., & Mejía-Amaya, A. F. (2024). Effect of artificial intelligence on the financial performance of Indian banking sector. Journal of Infrastructure, Policy and Development, 8(15), 9511. https://doi.org/10.24294/jipd9511

 40. Roberts, M. R., & Whited, T. M. (2011). Endogeneity in Empirical Corporate Finance. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.1748604

41. Saravanan, Mr. K. (2017). A REVIEW OF ARTIFICIAL INTELLIGENCЕ SYSTEMS. International Journal of Advanced Research in Computer Science, 8(9), 418–421. https://doi.org/10.26483/ijarcs.v8i9.5095

42. Serrasqueiro, Z., Nunes, P. M., & Da Silva, J. V. (2016). The Influence of Age and Size on Family-Owned Firms’ Financing Decisions: Empirical Evidence Using Panel Data. Long Range Planning, 49(6), 723–745. https://doi.org/10.1016/j.lrp.2015.12.012

43. Shiyyab, F. S., Alzoubi, A. B., Obidat, Q. M., & Alshurafat, H. (2023). The Impact of Artificial Intelligence Disclosure on Financial Performance. International Journal of Financial Studies, 11(3), 115. https://doi.org/10.3390/ijfs11030115

44. Stock, J. H., & Watson, M. W. (2008). Heteroskedasticity-Robust Standard Errors for Fixed Effects Panel Data Regression. Econometrica, 76(1), 155–174. https://doi.org/10.1111/j.0012-9682.2008.00821.x

45. Sunil Sapra, 2005. ""A regression error specification test (RESET) for generalized linear models"," Economics Bulletin, AccessEcon, vol. 3(1), pages 1-6.

46. Wamba-Taguimdje, S.-L., Fosso Wamba, S., Kala Kamdjoug, J. R., & Tchatchouang Wanko, C. E. (2020). Influence of artificial intelligence (AI) on firm performance: The business value of AI-based transformation projects. Business Process Management Journal, 26(7), 1893–1924. https://doi.org/10.1108/BPMJ-10-2019-0411

47. Xu, Q., & Xu, C. (2023). The Impact of Artificial Intelligence Strategy on Corporate Financial Performance—An Empirical Analysis Based on Listed Companies Panel Data. In N. Radojević, G. Xu, & D. D. H. K. H. Md Mansur (Eds.), Proceedings of the 2022 International Conference on Artificial Intelligence, Internet and Digital Economy (ICAID 2022) (Vol. 7, pp. 365–371). Atlantis Press International BV. https://doi.org/10.2991/978-94-6463-010-7_38

48. СПАРК [Электронный ресурс]: АО "Интерфакс". URL: https://spark- interfax.ru/#_top

49. Официальный сайт ЦБ РФ [Электронный ресурс]: Банк России. URL: https://cbr.ru/finorg/

50. ИСИЭЗ НИУ ВШЭ [Электронный ресурс]: Статистические сборники ВШЭ. URL: https://www.hse.ru/primarydata/ii/

 51. Коэффициент вариации (Variation coefficient). Loginom Wiki. URL: https://wiki.loginom.ru/articles/variation-coefficient.html#

52. Учебники Экономического факультета МГУ. Дружелюбная эконометрика. (Филипп Картаев, 2021) URL: https://books.econ.msu.ru/Introduction-to- Econometrics/chap08/8.4/

53. Лекция по эконометрике № 6. Эндогенность, инструментальные переменные. НИУ ВШЭ. Москва (О.А. Демидова, 2022) URL: https://www.hse.ru/mirror/pubs/share/565769150.pdf